Multi-objective optimization using Taguchi based grey relational analysis in turning of Rock dust reinforced Aluminum MMC

被引：95
作者：
Prakash, K. Soorya [1 ]
Gopal, P. M. [2 ]
Karthik, S. [3 ]
机构：
[1] Anna Univ, Dept Mech Engn, Reg Campus, Coimbatore 641046, Tamil Nadu, India
[2] Karpagam Acad Higher Educ, Dept Mech Engn, Coimbatore 641021, Tamil Nadu, India
[3] CIT Sandwich Polytech Coll, Dept Mech Engn, Coimbatore 641014, Tamil Nadu, India
来源：
MEASUREMENT | 2020年 / 157卷
关键词：
Aluminium; Rock Dust; Stir casting; Machining; Optimization; GRA; METAL-MATRIX COMPOSITES; SURFACE-ROUGHNESS; CUTTING FORCE; MACHINING PARAMETERS; MACHINABILITY; MICROSTRUCTURE; PARTICLES; INTEGRITY; WEAR;
D O I：
10.1016/j.measurement.2020.107664
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
The present work deals with the optimization of material and machining parameters for surface finish and Material Removal Rate (MRR) enhancements while turning Aluminium/Rock dust composite through Taguchi and Grey Relational Analysis (GRA). Rock dust particles weight percent (5, 10 & 15%) and particle size (10, 20 & 30 mu m) are varied accordingly to investigate the effect of reinforcement parameters on composite properties. Along with reinforcement weight % and particle size, also the turning parameters viz. speed, feed and depth of cut are chosen as input parameters with surface roughness (R-a) and MRR as responses. CNC turning is performed based on the L27 orthogonal array designed by Taguchi approach. Results expose that feed has remarkable effect on Ra and MRR rather than any of the other parameters examined. The optimum parameter combination identified through Multi criteria optimization technique GRA is S2W2N3F1D3 and the improvement in GRG approximates to 0.194. (C) 2020 Elsevier Ltd. All rights reserved.
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页数：13
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